Package Recipe 'bioconductor-hicbricks'

recipe bioconductor-hicbricks

Framework for Storing and Accessing Hi-C Data Through HDF Files

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/HiCBricks.html
License:: MIT + file LICENSE
Recipe:: /bioconductor-hicbricks/meta.yaml

HiCBricks is a library designed for handling large high-resolution Hi-C datasets. Over the years, the Hi-C field has experienced a rapid increase in the size and complexity of datasets. HiCBricks is meant to overcome the challenges related to the analysis of such large datasets within the R environment. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates example algorithms for calling domain boundaries and functions for high quality data visualization.

package bioconductor-hicbricks¶

Versions:

1.28.0-0, 1.24.0-0, 1.20.0-0, 1.18.0-0, 1.16.0-0, 1.11.0-0, 1.10.0-0, 1.8.0-1, 1.8.0-0,

1.28.0-0, 1.24.0-0, 1.20.0-0, 1.18.0-0, 1.16.0-0, 1.11.0-0, 1.10.0-0, 1.8.0-1, 1.8.0-0, 1.6.0-0, 1.4.0-0, 1.2.0-1, 1.0.0-0

Depends:

on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-genomicranges >=1.62.0,<1.63.0
on bioconductor-iranges >=2.44.0,<2.45.0
on bioconductor-rhdf5 >=2.54.0,<2.55.0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-seqinfo >=1.0.0,<1.1.0
on r-base >=4.5,<4.6.0a0
on r-curl
on r-data.table
on r-digest
on r-ggplot2
on r-jsonlite
on r-r.utils
on r-r6
on r-rcolorbrewer
on r-readr
on r-reshape2
on r-scales
on r-stringr
on r-tibble
on r-viridis

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-hicbricks

to add into an existing workspace instead, run:

pixi add bioconductor-hicbricks

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-hicbricks

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-hicbricks

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/bioconductor-hicbricks:<tag>

(see bioconductor-hicbricks/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-hicbricks/README.html)